STAT 104
4 credits
Introductory Statistics
Prerequisite(s): One of the following: (C or better in one of Principles of Mathematics 11, Applications of Mathematics 11, MATH 085, Foundations of Mathematics 11, Pre-calculus 11, Calculus 12, or Statistics 12) or (B or better in Workplace Mathematics 11, History of Mathematics 11, Apprenticeship Mathematics 12, or Apprenticeship and Workplace Mathematics 12) or (one of Foundations of Mathematics 12, Pre-calculus 12, Principles of Mathematics 12, or Applications of Mathematics 12) or (any UFV MATH course numbered 092 or higher) or (a score of 17/25 or better on Part A of the MSAT) or (45 university-level credits with department permission).
A basic introduction to descriptive statistics, probability, sampling, estimation, hypothesis testing, correlation, and regression. Recommended for anyone who wishes to evaluate research involving statistical analysis, especially students in humanities and social science. Using statistical computer software is essential to this course.
Note: As a general rule, students with Mathematics 11 are prepared to take STAT 104, those with Mathematics 12 are prepared to take STAT 106, and those with a full year of calculus are prepared to take STAT 270/MATH 270. Before registering, students should check the requirements of their program. The UFV Mathematics major program requires STAT 270, while the Mathematics minor program requires STAT 106 or STAT/MATH 270.
Note: Some degree and diploma credentials may allow only one of STAT 104 or STAT 106 to count as credit towards meeting program requirements.
STAT 106
4 credits
Statistics I
Prerequisite(s): One of the following: (C or better in one of Pre-calculus 11, Statistics 12, Calculus 12, Applications of Mathematics 12, Principles of Mathematics 12, Pre-calculus 12, MATH 092, MATH 096, MATH 110, MATH 124, or MATH 140) or (C or better in both MATH 094 and MATH 095) or (B or better in Foundations of Mathematics 12) or MATH 125 or (a score of 17/25 or better on Part B of the MSAT together with a score of 34/50 or better on Parts A and B combined).
An introduction to descriptive statistics, sampling, probability, estimation, hypothesis testing, correlation, regression, and analysis of variances, including multiple linear regression and one-way ANOVA. Facility with Grade 12 level algebra is expected, but no calculus is required.
Note: As a general rule, students with Mathematics 11 are prepared to take STAT 104, those with Mathematics 12 are prepared to take STAT 106, and those with a full year of calculus are prepared to take STAT 270/MATH 270. Before registering, students should check the requirements of their program. The UFV Mathematics major program requires STAT 270, while the Mathematics minor program requires STAT 106 or STAT 270.
Note: Some degree and diploma credentials may allow only one of STAT 104 or STAT 106 to count as credit towards meeting program requirements.
STAT 270
4 credits
Introduction to Probability and Statistics
Prerequisite(s): One of the following: MATH 112, MATH 118, or a B or better in MATH 141.
An introduction to the theory and practice of statistics for engineering and science students who have experience with calculus. Topics include descriptive statistics, probability, random variables and their probability distributions, sampling distributions, confidence intervals and hypothesis tests for means and proportions, Pearson’s Chi-squared tests, correlation, and linear regression.
Note: This course is offered as STAT 270 and MATH 270. Students may only take one of these for credit.
Note: As a general rule, students with Mathematics 11 are prepared to take STAT 104, those with Mathematics 12 are prepared to take STAT 106, and those with a full year of calculus are prepared to take STAT/MATH 270. Before registering, students should check the requirements of their program. The Mathematics major program requires STAT/MATH 270, while the Mathematics minor program requires STAT 106 or STAT/MATH 270.
STAT 271
3 credits
Introduction to Data Analysis and Statistical Modeling
Prerequisite(s): One of the following: STAT 104 with a B, STAT 106, or STAT 270.
A practical course on the modelling and analysis of statistical data using statistical software. Topics include graphical presentation, linear and nonlinear regression, Poisson log-linear and logistic regression, design and analysis of experiments, survival time analysis, and time series analysis.
Note: Students with credit for MATH 271 cannot take this course for further credit.
STAT 272
3 credits
Statistical Graphics and Languages
Prerequisite(s): One of the following: STAT 104 with a B, STAT 106, or STAT 270.
Introduces statistical graphics generated by powerful yet flexible statistical programming languages such as SAS and R. Students will learn the codes and procedures of these languages to write computer programs for producing these graphics, to manipulate data, to compute summary statistics, and to communicate results effectively in simple reports and presentations.
STAT 307
3 credits
Data Visualization
Prerequisite(s): One of the following: STAT 104 with a B or better, STAT 106, or STAT 270.
Communicate data to different audiences by creating and presenting data visualizations. Develop static, interactive, and animated charts and place them on a dashboard to convey a specific message or to let the audience explore the data by themselves. Tableau is used to design the data visualizations.
STAT 315
3 credits
Applied Regression Analysis
Prerequisite(s): STAT 270 or STAT 271.
Focuses on application of regression using statistical software. Topics include multiple regression, model building, screening methods, residual analysis, validation, analysis of covariance, splines, ridge, robust, nonparametric, and nonlinear regressions.
STAT 330
3 credits
Design of Experiments
Prerequisite(s): One of the following: STAT 104 with a B+ or better, STAT 106 with a B or better, STAT 270, or STAT 271.
Designing experiments, including factorial, 2^k, fractional and blocked experiments, confounding, fixed effects, random effects, mixed effects models, variance components. Statistical software is used for data analysis. Students design their own experiments and write a report on the resulting collection and analysis of data.
STAT 331
3 credits
Data Quality
Prerequisite(s): CIS 230 and one of the following: STAT 106 (formerly MATH 106) with a B, MATH 270/STAT 270, or STAT 271.
Data quality issues pertaining to data acquisition, storage, integrity, and use. Identifying and analyzing data quality problems, and assessing strategies and tools to correct them. Also covers privacy and security, and data quality needs of data warehousing and mining applications.
Note: This course is offered as COMP 331 and STAT 331 (formerly MATH 331). Students may take only one of these for credit.
Note: This course is offered as COMP 331 and STAT 331 (formerly MATH 331). Students may take only one of these for credit.
STAT 350
3 credits
Survey Sampling
Prerequisite(s): One of the following: STAT 106 with a B, STAT 104 with a B+, STAT 270, or STAT 271.
Simple random sampling, stratified, systematic and cluster sampling. Inference for averages, totals and percentages under these sampling conditions, including ratio, difference and regression estimation. Questionnaire design and estimation of population sizes (eg animal populations). Students produce reports on surveys using their own data, collected and analyzed according to course material.
Note: Students with credit for MATH 350 cannot take this course for further credit.
STAT 370
3 credits
Probability and Stochastic Processes
Prerequisite(s): MATH 211.
Theory of probability and stochastic processes for science and mathematics students. Topics include probability space, conditional probability and independence, continuous and discrete random variables, jointly distributed random variables, expectation,conditional expectation and properties, simulating data from distributions, limit theorems, Markov chains and Poisson processes, and Markov
Chains Monte Carlo (MCMC).
Note: This course is offered as STAT 370 and MATH 370. Students may only take one of these for credit.
STAT 402
3 credits
Applied Generalized Linear Models and Survival Analysis
Prerequisite(s): One of the following: STAT 271, MATH 302, or STAT 315
The course covers the application of the methods of the linear model analysis to non-normal data. This includes analysis of contingency tables using log-linear models, analysis of incidence data using Poisson models, analysis of binomial data using various link functions such as logit and probit, analysis of case-control data using logistic models, analysis of matched case-control data using logistic models, analysis of matched case-control data using conditional logistic regression, and analysis of survival data by adjusting for covariates or using Cox’s proportional hazard model.
Note: Students with credit for MATH 402 cannot take this course for further credit.
STAT 420
3 credits
Empirical and Non-Parametric Statistics
Prerequisite(s): One of STAT 270, STAT 271, STAT 315, or STAT 330.
Introduction to various non-parametric techniques to test parameters for location and dispersion, including problems in single sample, two or more independent samples, and two or more related samples. Non-parametric inferential procedures are used when the assumptions underlying parametric tests are invalid. Goodness-of-fit tests and tests of association are also discussed.
STAT 430
3 credits
Time Series and Forecasting
Prerequisite(s): STAT 315 or STAT 271.
Introduces the basic ideas of time series analysis and forecasting methods. Topics include stationarity, autocovariance, autocorrelation and partial autocorrelation functions, and the Box-Jenkins classical models. Focuses on the practical implementation of the methods and analysis of real-life time series data using statistical software.
STAT 431
3 credits
Data Mining
Prerequisite(s): COMP 230 (formerly CIS 230), STAT 271, and STAT 331/COMP 331.
Data mining provides the techniques of extracting useful information and hidden patterns from a massive amount of data. Main topics include data exploration, classification, decision trees, Bayesian classifiers, frequent item sets, association rules, clustering, K-means, EM algorithm, and anomaly detection.
Note: This course is offered as STAT 431 and COMP 431. Students may take only one of these for credit.
STAT 450
3 credits
Statistical Theory
Prerequisite(s): MATH 370/STAT 370 or (MATH 270/STAT 270 and MATH 211).
A course in mathematical statistics. Distributions of functions of random variables; transformations; beta, t, F, multivariate normal distributions; order statistics; convergence in distribution and probability; Law of Large Numbers; Central Limit Theorem; method of maximum likelihood; inference.
Note: This course is offered as STAT 450 and MATH 450. Students may only take one of these for credit.
STAT 470
3 credits
Applied Multivariate Statistical Analysis
Prerequisite(s): One of the following: STAT 271, STAT 315, or STAT 330.
Focuses on a range of widely-used multivariate statistical techniques, their relationship with familiar univariate methods, and the solution to practical problems using statistical software. Topics include Hotelling’s T2, MANOVA, multivariate regression, principal components, factor analysis, and discrimination and classification analysis.
STAT 488
3 credits
Selected Topics in Statistics
Prerequisite(s): At least three upper-level STAT courses, and at least one additional upper-level course labeled MATH or STAT. Certain programs of study may require more particular prerequisites. The written permission of the instructor is required.
This course is designed for students who wish to examine in greater depth a particular statistical technique or application. It will be offered either as an individual reading course or as a seminar, depending upon student and faculty interest. May not be repeated for additional credit.
Note: Students with credit for MATH 488 cannot take this course for further credit.
